Assessing agentic AI risks with multi-layered governance
Agentic AI demands a different approach to governance—proactive, structured, layered.
AI agents are hitting reality checks faster than the hype suggested
Agentic AI fails due to unrealistic expectations about automation capabilities, poor use case selection, data quality problems across multiple sources, and governance gaps requiring custom solutions.
NVIDIA shows what works when companies scale AI agents
Companies seeing real returns from AI agents build measurement systems alongside the technology, treating deployment as architectural decisions rather than bolt-on solutions.
Why the difference between AI agents and agentic AI matters for product teams
I keep seeing teams conflate AI agents with agentic AI, and this distinction matters more than most realize. One's a contained service; the other's a network of intelligent actors making collective decisions.
AI agents need their own authentication layer
Scalekit raised $5.5M to build authentication for AI agents. With Gartner predicting 25% of breaches by 2028 will involve compromised agents, traditional identity management needs an upgrade for autonomous workflows.
Why splitting AI agents into thinkers and doers actually works
Developer discovers splitting AI into planning and execution agents beats monolithic design for complex voice tasks like restaurant reservations.
Agentic AI accountability creates a genuine management puzzle
AI accountability isn't just about rules—it's about redesigning management for systems that move faster than human judgment.
AI training environments create new liability exposure for product teams
Anthropic's $1B RL environment budget signals training phase liability issues. When agents learn in simulated workflows, who owns the resulting IP? Product teams need training data governance before shopping for agent capabilities.
